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Econometric Analysis of Count Data / by Rainer Winkelmann
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Edition: Fifth editionDescription: Online-Ressource (digital)ISBN:- 9783540783893
- 330.015195
- 330.0182
- 330
- HB139-141
- HB139
Contents:
Summary: The book provides graduate students and researchers with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and cluPPN: PPN: 1646554876Package identifier: Produktsigel: ZDB-2-SBE
Introduction; Probability Models for Count Data; Poisson Regression; Unobserved Heterogeneity; Sample Selection and Endogeneity; Zeros in Count Data Models; Correlated Count Data; Bayesian Analysis of Count Data; Applications
CoverPreface -- Contents -- 1 Introduction -- 1.1 Poisson Regression Model -- 1.2 Examples -- 1.3 Organization of the Book -- 2 Probability Models for Count Data -- 2.1 Introduction -- 2.2 Poisson Distribution -- 2.2.1 Definitions and Properties -- 2.2.2 Genesis of the Poisson Distribution -- 2.2.3 Poisson Process -- 2.2.4 Generalizations of the Poisson Process -- 2.2.5 Poisson Distribution as a Binomial Limit -- 2.2.6 Exponential Interarrival Times -- 2.2.7 Non-Poissonness -- 2.3 Further Distributions for Count Data -- 2.3.1 Negative Binomial Distribution -- 2.3.2 Binomial Distribution -- 2.3.3 Logarithmic Distribution -- 2.3.4 Summary -- 2.4 Modified Count Data Distributions -- 2.4.1 Truncation -- 2.4.2 Censoring and Grouping -- 2.4.3 Altered Distributions -- 2.5 Generalizations -- 2.5.1 Mixture Distributions -- 2.5.2 Compound Distributions -- 2.5.3 Birth Process Generalizations -- 2.5.4 Katz Family of Distributions --^2.5.5 Additive Log-Differenced Probability Models -- 2.5.6 Linear Exponential Families -- 2.5.7 Summary -- 2.6 Distributions for Over- and Underdispersion -- 2.6.1 Generalized Event Count Model -- 2.6.2 Generalized Poisson Distribution -- 2.6.3 Poisson Polynomial Distribution -- 2.6.4 Double Poisson Distribution -- 2.6.5 Summary -- 2.7 Duration Analysis and Count Data -- 2.7.1 Distributions for Interarrival Times -- 2.7.2 Renewal Processes -- 2.7.3 Gamma Count Distribution -- 2.7.4 Duration Mixture Models -- 3 Poisson Regression -- 3.1 Specification -- 3.1.1 Introduction -- 3.1.2 Assumptions of the Poisson Regression Model -- 3.1.3 Ordinary Least Squares and Other Alternatives -- 3.1.4 Interpretation of Parameters -- 3.1.5 Period at Risk -- 3.2 Maximum Likelihood Estimation -- 3.2.1 Introduction -- 3.2.2 Likelihood Function and Maximization -- 3.2.3 Newton-Raphson Algorithm -- 3.2.4 Properties of the Maximum Likelihood Estimator -- 3.2.5 Estimation of the Variance Matrix --^3.2.6 Approximate Distribution of the Poisson Regression Coefficients -- 3.2.7 Bias Reduction Techniques -- 3.3 Pseudo-Maximum Likelihood -- 3.3.1 Linear Exponential Families -- 3.3.2 Biased Poisson Maximum Likelihood Inference -- 3.3.3 Robust Poisson Regression -- 3.3.4 Non-Parametric Variance Estimation -- 3.3.5 Poisson Regression and Log-Linear Models -- 3.3.6 Generalized Method of Moments -- 3.4 Sources of Misspecification -- 3.4.1 Mean Function -- 3.4.2 Unobserved Heterogeneity -- 3.4.3 Measurement Error -- 3.4.4 Dependent Process -- 3.4.5 Selectivity -- 3.4.6 Simultaneity and Endogeneity -- 3.4.7 Underreporting -- 3.4.8 Excess Zeros -- 3.4.9 Variance Function -- 3.5 Testing for Misspecification -- 3.5.1 Classical Specification Tests -- 3.5.2 Regression Based Tests -- 3.5.3 Goodness-of-Fit Tests -- T.
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